Machine learning based parametrization of the resolution function for the first experimental area of the n_TOF facility at CERN

Abstract

This study addresses a challenge of parametrizing a resolution function of a neutron beam from the neutron time of flight facility n_TOF at CERN. A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape, over approximately ten orders of magnitude in neutron energy. To avoid a need for a manual identification of the appropriate analytical forms—hindering past attempts at its parametrization—we take advantage of the versatile machine learning techniques. Specifically, we parametrized it by training a multilayer feedforward neural network, relying on a key idea that such network acts as a universal approximator. The proof-of-concept is presented for a resolution function for the first experimental area of the n_TOF facility from the third phase of its operation. We propose an optimal network structure for a resolution function in question, which is also expected to be optimal or near-optimal for other experimental areas and for different phases of n_TOF operation. To reconstruct several resolution function forms in common use from a single parametrized form, we provide a practical tool in the form of a specialized C++ class encapsulating the computationally efficient procedures suited to the task.